Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2020 Nov 27;20(23):6785. doi: 10.3390/s20236785.
Visual sorting of express parcels in complex scenes has always been a key issue in intelligent logistics sorting systems. With existing methods, it is still difficult to achieve fast and accurate sorting of disorderly stacked parcels. In order to achieve accurate detection and efficient sorting of disorderly stacked express parcels, we propose a robot sorting method based on multi-task deep learning. Firstly, a lightweight object detection network model is proposed to improve the real-time performance of the system. A scale variable and the joint weights of the network are used to sparsify the model and automatically identify unimportant channels. Pruning strategies are used to reduce the model size and increase the speed of detection without losing accuracy. Then, an optimal sorting position and pose estimation network model based on multi-task deep learning is proposed. Using an end-to-end network structure, the optimal sorting positions and poses of express parcels are estimated in real time by combining pose and position information for joint training. It is proved that this model can further improve the sorting accuracy. Finally, the accuracy and real-time performance of this method are verified by robotic sorting experiments.
复杂场景下的快递包裹快速视觉分拣一直是智能物流分拣系统的关键问题。现有方法仍然难以实现对无序堆叠包裹的快速、准确分拣。为了实现对无序堆叠快递包裹的准确检测和高效分拣，我们提出了一种基于多任务深度学习的机器人分拣方法。首先，提出了一种轻量级目标检测网络模型，以提高系统的实时性。通过使用尺度变量和网络联合权重对模型进行稀疏化，并自动识别不重要的通道。采用剪枝策略减少模型的大小，提高检测速度，同时不损失精度。然后，提出了一种基于多任务深度学习的最优分拣位置和姿态估计网络模型。该模型采用端到端网络结构，通过结合姿态和位置信息进行联合训练，实时估计快递包裹的最优分拣位置和姿态。实验证明，该模型可以进一步提高分拣精度。最后，通过机器人分拣实验验证了该方法的准确性和实时性。
